How to avoid augmenting data in validation split of Keras ImageDataGenerator?

Question

I'm using the following generator:

datagen = ImageDataGenerator(
    fill_mode='nearest',
    cval=0,
    rescale=1. / 255,
    rotation_range=90,
    width_shift_range=0.1,
    height_shift_range=0.1,
    zoom_range=0.5,
    horizontal_flip=True,
    vertical_flip=True,
    validation_split = 0.5,
)

train_generator = datagen.flow_from_dataframe(
    dataframe=traindf,
    directory=train_path,
    x_col="id",
    y_col=classes,
    subset="training",
    batch_size=8,
    seed=123,
    shuffle=True,
    class_mode="other",
    target_size=(64,64))


STEP_SIZE_TRAIN = train_generator.n // train_generator.batch_size

valid_generator = datagen.flow_from_dataframe(
    dataframe=traindf,
    directory=train_path,
    x_col="id",
    y_col=classes,
    subset="validation",
    batch_size=8,
    seed=123,
    shuffle=True,
    class_mode="raw",
    target_size=(64, 64))

STEP_SIZE_VALID = valid_generator.n // valid_generator.batch_size

Now the problem is that the validation data is also being augmented which I guess is not something you'd want to do while training. How do I avoid this? I don't have two directories for train and validation. I want to use a single dataframe to train the network. Any suggestions?

Answer 1

The solution my friend found was using a different generator but with the same validation split and no shuffle.

datagen = ImageDataGenerator(
    #featurewise_center=True,
    #featurewise_std_normalization=True,
    rescale=1. / 255,
    rotation_range=90,
    width_shift_range=0.1,
    height_shift_range=0.1,
    zoom_range=0.5,
    horizontal_flip=True,
    vertical_flip=True,
    validation_split = 0.15,
)

valid_datagen=ImageDataGenerator(rescale=1./255,validation_split=0.15)

and then you can define the two generators as

train_generator = datagen.flow_from_dataframe(
    dataframe=traindf,
    directory=train_path,
    x_col="id",
    y_col=classes,
    subset="training",
    batch_size=64,
    seed=123,
    shuffle=False,
    class_mode="raw",
    target_size=(224,224))


STEP_SIZE_TRAIN = train_generator.n // train_generator.batch_size

valid_generator = valid_datagen.flow_from_dataframe(
    dataframe=traindf,
    directory=train_path,
    x_col="id",
    y_col=classes,
    subset="validation",
    batch_size=64,
    seed=123,
    shuffle=False,
    class_mode="raw",
    target_size=(224, 224))

STEP_SIZE_VALID = valid_generator.n // valid_generator.batch_size